Field of the Invention
The present invention is related to providing weather forecasts on wide geographic areas, and more particularly, to tailoring weather forecasts to infrastructure vulnerabilities for a wide geographic area.
Background Description
Typically, weather is forecast from weather data collected from satellite data and from sensors that may be located over a large geographic area or even worldwide. In forecasting the weather for a wide geographic area, the area typically is divided into smaller more manageable units by superimposing a grid over the area. The finer the grid (i.e., smaller cells), the more precise the area forecast. The relationship of the weather data among the several units or grid locations describes the area forecast in several algebraic equations, e.g., using a finite element approach for forecasting weather for the gridded area. Typical finite element approaches or methods (or weather forecasting numerical solvers), are known in the art, for example, as Finite Element Modeling (FEM), Finite Element Analysis (FEA), and Finite Differences Method.
Frequently, the weather model for a very precise forecast requires considerable, even excessive, data processing resources to arrive at a solution. The higher the grid resolution, the larger the number of units, the more complex the model equations and, correspondingly, the more data processing resources consumed in forecasting weather. Thus, data processing demands may make providing real time or even timely forecasts infeasible with any precision for a large area. This becomes especially troublesome when, as is commonly the case, forecast results are subject to tight delivery deadlines. Consequently, a type of targeted mesh refinement, commonly known as adaptive mesh refinement (AMR), has been used to selectively provide real time forecasts.
Adaptive mesh refinement begins with a low resolution grid for an area. The weather map contains coarse-grained cells to provide rough initial forecasts. Where more detailed forecasts are necessary for certain cells, provided there is sufficient data and time available, those cells are further refined. Typical cell refinement is based on quality and quantity of sensors in the area, i.e., the focus is on areas with more and better sensors. Plale et al., “CASA and LEAD: Adaptive Cyberinfrastructure for Real-Time Multiscale Weather Forecasting,” IEEE Computer Magazine, 2006, provides an example of sensor based refinement.
Unfortunately, weather for an area is a segment of an open, unstable and targeted system and any selected grid may cause a divergent result, i.e., an unsolvable mesh. Each divergent result requires redesigning the grid and solving the resulting FEM. There is no guarantee that the FEM will converge on a solution even on a redesigned grid. However, even when redesigning the grid in sensor based refinement arrives at a solution that weather solution still focuses on refining the grid based on the sensors, i.e. sensor quality and quantity. Thus, the final refined grid still may not conform forecast precision to where actual interests may lie.
State of the art approaches are directed to providing a general forecast for an area that has use to everyone in the area, and are not centered on local infrastructure sensitivities, for example. A typical forecast is made independent of, and oblivious to, local infrastructure sensitivities. State of the art power companies, for example, distribute power over area grids that may include buried as well as areal power lines connected to power stations and substations. All of these power company elements include wires, transformers, fuses, capacitor banks and other assets that are vulnerable, for example, to weather related water damage. Other utilities, such as land and cellular telephone networks, may have the same or similar vulnerabilities.
The typical, sensor-based refinement criteria may either miss areas important to local infrastructure, and include areas that are unimportant to infrastructure concerns. Thus, sensor based refinement may not arrive at an acceptable forecast for infrastructure management. Consequently, using sensor based refinement frequently has required additional work for infrastructure management, e.g., additional solutions directed to missed areas and adding cost.
Thus, there is a need for efficiently providing weather forecasts for large areas targeted for unevenly distributed area infrastructure; and, more particularly for efficiently and quickly arriving at accurate weather forecasts for large areas that are targeted to areas where weather may have the most impact on local utility infrastructure.